Classifying Sub-Parcel Grassland Management Practices by Optical and Microwave Remote Sensing

Abstract

Grassland management practices and intensities are key factors influencing the quality and balance of their provisioning and regulating ecosystem services. Most European temperate grasslands are exploited through mowing, grazing, or a combination of both in relatively small management units. Grazing and mowing can however not be considered equivalent because the first is gradual and selective and the second is not. In this study, the aim is to differentiate grasslands in terms of management practices and to retrieve homogeneous management units. Grasslands are classified hierarchically, first through a pixel-based supervised classification to differentiate grazed pastures from mown hay meadows and then through an object-based mowing detection method to retrieve the timing and frequency of mowing events. A large field dataset was used to calibrate and validate the method. For the classification, 18 different input feature combinations derived from Sentinel-1 and Sentinel-2 were tested for a random forest classifier through a cross-validation scheme. The best results were obtained based on the Leaf Area Index (LAI) times series with cubic spline interpolation. The classification differentiated pastures (grazed) from hay meadows (mown) with an overall accuracy of 88%. The classification is then combined with the existing parcel delineation and high-resolution ancillary data to retrieve the homogeneous management units, which are used for the object-based mowing detection based on the Sentinel-1 coherence and Sentinel-2 NDVI. The mowing detection performances were increased thanks to the grassland mask, the management unit delineation, and the exclusion of pastures, reaching a precision of 93% and a detection rate of 82%. This hierarchical grassland classification approach allowed to differentiate three types of grasslands, namely pastures, and meadows (including mixed practices) with an early first mowing event and with a late first mowing event, with an overall accuracy of 79%. The grasslands could be further differentiated by mowing frequency, resulting in five final classes.